Course Outline
Preparing Machine Learning Models for Deployment
- Packaging models using Docker
- Exporting models from TensorFlow and PyTorch
- Considerations for versioning and storage
Serving Models on Kubernetes
- An overview of inference servers
- Deploying TensorFlow Serving and TorchServe
- Configuring model endpoints
Optimizing Inference Performance
- Strategies for batching
- Handling concurrent requests
- Tuning for latency and throughput
Autoscaling ML Workloads
- Horizontal Pod Autoscaler (HPA)
- Vertical Pod Autoscaler (VPA)
- Kubernetes Event-Driven Autoscaling (KEDA)
Managing GPU Provisioning and Resources
- Configuration of GPU nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML workloads
Model Rollout and Release Strategies
- Blue/green deployments
- Canary rollout patterns
- A/B testing for model evaluation
Monitoring and Observability for Production ML
- Key metrics for inference workloads
- Best practices for logging and tracing
- Creating dashboards and setting up alerting
Security and Reliability Considerations
- Securing model endpoints
- Implementing network policies and access control
- Ensuring high availability
Summary and Next Steps
Requirements
- A solid grasp of containerized application workflows.
- Practical experience with Python-based machine learning models.
- Foundational knowledge of Kubernetes.
Target Audience
- ML engineers
- DevOps engineers
- Platform engineering teams
Custom Corporate Training
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- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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The knowledge and exchanges with Augustin
